在現今社會中，高壓電力設備被廣泛地使用在生活周遭，因此對於電力設備安全性更需要加倍留心。其中，局部放電訊號出現是其中一重要警訊，此訊號可能代表著設備有故障之疑。因此，透過局部放電檢測與訊號分析，可藉此進行設備之狀態評估。
局部放電訊號是一種時變訊號，所以多數研究著重於時頻分析方法，藉由能量─時間─頻率之相對關係探討放電種類或強度。本論文主要為設計一整合訊號分析系統，其中包括常見之時頻方法與本文提出之分析方法，再搭配可分離外部訊號的音射感測器組，透過此分析系統進一步萃取出放電資訊。
在本文提出之分析方法中，可分成兩類，分別為快速診斷法與相關性特徵萃取法。前者是利用放電訊號與非放電訊號的頻譜變化找出頻率間之相對關係，藉此快速判斷是否為局部放電訊號，進而決定是否需要更精準之進一步分析。後者則主要是利用經驗模態分解法找出訊號所隱含的模態，並利用兩訊號間的模態相關性找出其共通模態，達到實際訊號的純化。針對時域相關性與頻域相關性，本文提出相關經驗模態分解與頻譜相關模態分解，並加入總體經驗模態分解法來改善傳統經驗模態分析的缺點。經過以上幾種相關模態分解法取出之模態，最後可合成局部放電訊號之真實模態。
最後，利用各方法分析實際放電訊號，藉此觀察各分析方法之特長。在結果中，儘管各分析方法展現方式不同，都可展現出局部放電訊號能量之變化，但是結果顯示，透過自製音射感測器組搭配所提出之相關性經驗模態分解法，可更精確找出放電基本模態並將雜訊去除。未來，透過純化的經驗模態結果可以增加分析資料量並建立一局部放電訊號資料庫，藉以透過此系統快速判定放電模式並適時提出警訊，將可大幅增加設備與用電安全。

Currently, the demand for more and more energy power has led to a great increase in the equipments used in our living environment, and signifies that greater electrical security is necessary. For the high-power equipment, partial discharge (PD) is an early warning signal of equipment failure. The appearance of PD means the insulation capability of the power equipment is insufficient for such high-stress operation. If no any protection measure was taken, a series of PDs may lead to a serious catastrophe. To prevent such an unexpected accident, the best way is to carry out a precisely diagnosis.
For a time-variant signal, time-frequency analysis methods are the better choice; therefore, a PD analysis system contains functions of most commonly used time-frequency analysis methods and four new developed methods is proposed in this dissertation. The system can be used to extract the experience mode of PD signals and provides the information to judge the status of power equipment.
The proposed algorithms can be divided into two groups: fast diagnosis method and correlation-characteristic extraction method. Based on the spectrum variation between a PD signal and a normal signal unaffected by any discharge, the energy ratio is defined as a discrimination factor and used for evaluation. When this ratio is changed, the signal will be determined as abnormal data. Base on the judgment results, the suspicious cases can be assign for further analysis. For the correlation-characteristic extraction method, the implied vibration modes of two correlated input signals demodulated from the PD signal with empirical mode decomposition method can be sorted according to the correlation between any two modes. By choosing the high correlated intrinsic mode functions, PD signal purification can be achieved. Because the time-domain correlation and frequency-domain correlation have the individual physical meaning, there are two types of correlation empirical mode decomposition methods proposed, named the correlated empirical mode decomposition method (CEMD) and the spectral correlated empirical mode decomposition (SCEMD). Additionally, the ensemble empirical mode decomposition method was integrated within the CEMD method to decrease the mode mixing effect, and defined as the correlated ensemble empirical mode decomposition method (CEEMD). After the correlation decomposition, these chosen empirical modes can be assembled to generate an acoustic signal that can be taken as the pure PD signal.
In this dissertation, all of the above mentioned analysis methods were used to analyze the actual PD signals, the differences between these methods were also discussed. The analyzing results of commonly used time-frequency methods show similar energy variation with different forms. But by using a new developed acoustic signal sensor pair and the correlation empirical mode decomposition methods, the results show a potential way to evaluate the PD signal. After collecting more pure PD signals, a database will be set up for data comparison. Furthermore, a precise and efficient data diagnosis can determine the PD type and suggest the right replacement time to increase the electrical security and decrease the possibility of incurring damage.

[1] R. Bartnikas, "Partial discharges. Their mechanism, detection and measurement," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 9, pp. 763-808, 2002.
[2] W. R. Si, J. H. Li, D. J. Li, J. G. Yang, and Y. M. Li, "Investigation of a comprehensive identification method used in acoustic detection system for GIS," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 17, pp. 721-732, 2010.
[3] J. S. Pearson, O. Farish, B. F. Hampton, M. D. Judd, D. Templeton, B. W. Pryor, and I. M. Welch, "Partial discharge diagnostics for gas insulated substations," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 2, pp. 893-905, 1995.
[4] S. M. Neuhold, M. L. Schmatz, M. Hassig, M. M. Spuhler, and G. Storf, "Combined broad and narrow band multichannel PD measurement system with high sensitivity for GIS," in 1999 Eleventh International Symposium on High Voltage Engineering, 1999, pp. 152-155 vol.5.
[5] M. Kawada, A. Tungkanawanich, Z. I. Kawasaki, and K. Matsu-Ura, "Detection of wide-band E-M signals emitted from partial discharge occurring in GIS using wavelet transform," IEEE Transactions on Power Delivery, vol. 15, pp. 467-471, 2000.
[6] M. D. Judd, "Using finite difference time domain techniques to model electrical discharge phenomena," in 2000 Annual Report Conference on Electrical Insulation and Dielectric Phenomena, 2000, pp. 518-521 vol.2.
[7] T. Hoshino, K. Kato, N. Hayakawa, and H. Okubo, "A novel technique for detecting electromagnetic wave caused by partial discharge in GIS," IEEE Transactions on Power Delivery, vol. 16, pp. 545-551, 2001.
[8] S. Okabe, S. Kaneko, M. Yoshimura, H. Muto, C. Nishida, and M. Kamei, "Partial discharge diagnosis method using electromagnetic wave mode transformation in gas insulated switchgear," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 14, pp. 702-709, 2007.
[9] S. Kaneko, S. Okabe, M. Yoshimura, H. Muto, C. Nishida, and M. Kamei, "Partial discharge diagnosis method using electromagnetic wave mode transformation in actual GIS structure," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 15, pp. 1329-1339, 2008.
[10] L. E. Lundgaard, "Partial discharge. XIII. Acoustic partial discharge detection-fundamental considerations," Electrical Insulation Magazine, IEEE, vol. 8, pp. 25-31, 1992.
[11] A. Schei, S. Kyrkjeeide, and V. Larsen, "Acoustic insulation analyzer for periodic condition assessment of Gas Insulated Substations," in Transmission and Distribution Conference and Exhibition 2002: Asia Pacific. IEEE/PES, 2002, pp. 919-924 vol.2.
[12] C.-C. Su, "A New Acoustic Emission System for the Detection, Identification and Location of Partial Discharge in Cast-Resin Dry-type Transformers," Ph.D. dissertation, Department of Electrical Engineering, National Cheng Kung University, 2010.
[13] V. G. Arakelian and A. J. Kovalenko, "Operative physico-chemical diagnostics of SF6-equipment," in 2000 IEEE International Symposium on Electrical Insulation, 2000, pp. 320-323.
[14] Z. Xiao-xing, L. Wang-ting, T. Ju, and X. Peng, "Study on PD detection in SF6 using multi-wall carbon nanotube films sensor," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 17, pp. 833-838, 2010.
[15] E. Carminati, L. Cristaldi, M. Lazzaroni, and A. Monti, "A neuro-fuzzy approach for the detection of partial discharge," IEEE Transactions on Instrumentation and Measurement, vol. 50, pp. 1413-1417, 2001.
[16] T. K. Abdel-Galil, R. M. Sharkawy, M. M. A. Salama, and R. Bartnikas, "Partial discharge pattern classification using the fuzzy decision tree approach," IEEE Transactions on Instrumentation and Measurement, vol. 54, pp. 2258-2263, 2005.
[17] A. Contin, A. Cavallini, G. C. Montanari, G. Pasini, and F. Puletti, "Digital detection and fuzzy classification of partial discharge signals," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 9, pp. 335-348, 2002.
[18] M. Kawada, "Ultra wide band VHF/UHF radio interferometer system for detecting partial discharge source," in Power Engineering Society Winter Meeting, 2002. IEEE, 2002, pp. 1482-1487 vol.2.
[19] Z. Xu, J. Tang, and C. Sun, "Application of Complex Wavelet Transform to Suppress White Noise in GIS UHF PD Signals," IEEE Transactions on Power Delivery, vol. 22, pp. 1498-1504, 2007.
[20] C. S. Chang, J. Jin, C. Chang, T. Hoshino, M. Hanai, and N. Kobayashi, "Separation of corona using wavelet packet transform and neural network for detection of partial discharge in gas-insulated substations," IEEE Transactions on Power Delivery, vol. 20, pp. 1363-1369, 2005.
[21] R. Sharkawy, T. Abdel-Galil, R. Mangoubi, M. Salama, and R. Bartnikas, "Particle identification in terms of acoustic partial discharge measurements in transformer oils," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 15, pp. 1649-1656, 2008.
[22] L. Hao, P. L. Lewin, and S. G. Swingler, "Improving detection sensitivity for partial discharge monitoring of high voltage equipment," Measurement Science and Technology, vol. 19, p. 055707, 2008.
[23] L. Hao and P. L. Lewin, "Partial discharge source discrimination using a support vector machine," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 17, pp. 189-197, 2010.
[24] Z. Xiaoxing, R. Jiangbo, T. Ju, and S. Caixin, "Kernel statistical uncorrelated optimum discriminant vectors algorithm for GIS PD recognition," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 16, pp. 206-213, 2009.
[25] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis," Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 454, p. 903, 1998.
[26] W. Xiaodong, L. Baoqing, L. Zhiwei, H. T. Roman, O. L. Russo, K. K. Chin, and K. R. Farmer, "Analysis of partial discharge signal using the Hilbert-Huang transform," IEEE Transactions on Power Delivery, vol. 21, pp. 1063-1067, 2006.
[27] M.-H. Wang, "High-Frequency Current Transformer Amplifying and Filtering Circuit for Partial Discharge Detection " MS thesis, Department of Electrical Engineering, National Cheng Kung University, 2008.
[28] G. Camilli, G. S. Gordon, and R. E. Plump, "Gaseous Insulation for High-Voltage Transformers," Power Apparatus and Systems, Part III. Transactions of the American Institute of Electrical Engineers, vol. 71, pp. 348-357, 1952.
[29] R. Baumgartner, B. Fruth, W. Lanz, and K. Pettersson, "Partial discharge. IX. PD in gas-insulated substations-fundamental considerations," Electrical Insulation Magazine, IEEE, vol. 7, pp. 5-13, 1991.
[30] M. Masanori, T. Okano, S. Nishimoto, I. Kitani, and K. Arii, "Study on application of wavelet analysis for degradation diagnosis of partial discharge in a void," in Conduction and Breakdown in Solid Dielectrics, 1995. ICSD'95., Proceedings of the 1995 IEEE 5th International Conference on, 1995, pp. 371-375.
[31] A. V. Hippel, "Electric Breakdown of Solid and Liquid Insulators," vol. 8, pp. 815-832, 1937.
[32] I. A. Metwally, "Status review on partial discharge measurement techniques in gas-insulated switchgear/lines," Electric Power Systems Research, vol. 69, pp. 25-36, 2004.
[33] L. Niemeyer, "A generalized approach to partial discharge modeling," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 2, pp. 510-528, 1995.
[34] T. Okamoto, T. Kato, Y. Yokomizu, Y. Suzuoki, and T. Tanaka, "PD characteristics as a stochastic process and its integral equation under sinusoidal voltage," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 8, pp. 82-90, 2001.
[35] T. Takada, "Acoustic and optical methods for measuring electric charge distributions in dielectrics," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 6, pp. 519-547, 1999.
[36] H. Yuji, A. Hiroyuki, T. Yasuhiro, T. Tatsuo, and M. Yoshinao, "Space Charge Formation in LDPE/MgO Nano-composite Thin Film under Ultra-high DC Electric Stress," in Properties and applications of Dielectric Materials, 2006. 8th International Conference on, 2006, pp. 159-162.
[37] H. Miyake, Y. Tanaka, and T. Takada, "Characteristic of charge accumulation in glass materials under electron beam irradiation," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 14, pp. 520-528, 2007.
[38] R. Bartnikas and J. P. Novak, "On the spark to pseudoglow and glow transition mechanism and discharge detectability," IEEE Transactions on Electrical Insulation, vol. 27, pp. 3-14, 1992.
[39] A. Pedersen, G. C. Crichton, and I. W. McAllister, "The theory and measurement of partial discharge transients," IEEE Transactions on Electrical Insulation, vol. 26, pp. 487-497, 1991.
[40] L. E. Lundgaard, M. Runde, and B. Skyberg, "Acoustic diagnosis of gas insulated substations: a theoretical and experimental basis," IEEE Transactions on Power Delivery, vol. 5, pp. 1751-1759, 1990.
[41] R. Baumgartner, B. Fruth, W. Lanz, and K. Pettersson, "Partial discharge. X. PD in gas-insulated substations-measurement and practical considerations," Electrical Insulation Magazine, IEEE, vol. 8, pp. 16-27, 1992.
[42] L. E. Lundgaard, "Partial discharge. XIV. Acoustic partial discharge detection-practical application," Electrical Insulation Magazine, IEEE, vol. 8, pp. 34-43, 1992.
[43] M. E. Haque, A. B. Darus, M. M. Yaacob, and F. Ahmed, "Diagnosis of GIS based on acoustic sensing technique," in Advances in Power System Control, Operation and Management, 1997. APSCOM-97. Fourth International Conference on (Conf. Publ. No. 450), 1997, pp. 556-558 vol.2.
[44] E. Haque, A. B. Darus, M. M. Yaacob, and F. Ahmed, "Application of acoustic sensing and signal processing for PD detection in GIS," in Information, Communications and Signal Processing, 1997. ICICS., Proceedings of 1997 International Conference on, 1997, pp. 745-749 vol.2.
[45] Z. Wu and N. E. Huang, "Ensemble empirical mode decomposition: A noise-assisted data analysis method," Advances in Adaptive Data Analysis, vol. 1, pp. 1-41, 2009.
[46] Y. Meyer, "Ondettes et Functions Splines," Lectures given at the University of Torino, Italy, 1986.
[47] A. Pigorini, A. G. Casali, S. Casarotto, F. Ferrarelli, G. Baselli, M. Mariotti, M. Massimini, and M. Rosanova, "Time-frequency spectral analysis of TMS-evoked EEG oscillations by means of Hilbert-Huang transform," Journal of Neuroscience Methods, vol. 198, pp. 236-245, 2011.
[48] D. Yinfeng, L. Yingmin, X. Mingkui, and L. Ming, "Analysis of earthquake ground motions using an improved Hilbert-Huang transform," Soil Dynamics and Earthquake Engineering, vol. 28, pp. 7-19, 2008.
[49] X. Li, D. Li, Z. Liang, L. J. Voss, and J. W. Sleigh, "Analysis of depth of anesthesia with Hilbert-Huang spectral entropy," Clinical Neurophysiology, vol. 119, pp. 2465-2475, 2008.
[50] Z. K. Peng, P. W. Tse, and F. L. Chu, "A comparison study of improved Hilbert-Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing," Mechanical Systems and Signal Processing, vol. 19, pp. 974-988, 2005.
[51] V. K. Rai and A. R. Mohanty, "Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform," Mechanical Systems and Signal Processing, vol. 21, pp. 2607-2615, 2007.
[52] N. E. Huang and Z. Wu, "A review on Hilbert-Huang transform: Method and its applications to geophysical studies," Rev. Geophys., vol. 46, p. RG2006, 2008.